Sensor alignment

ABSTRACT

Described herein are systems, methods, and non-transitory computer readable media for performing an alignment between a first vehicle sensor and a second vehicle sensor. Two-dimensional (2D) data indicative of a scene within an environment being traversed by a vehicle is captured by the first vehicle sensor such as a camera or a collection of multiple cameras within a sensor assembly. A three-dimensional (3D) representation of the scene is constructed using the 2D data. 3D point cloud data also indicative of the scene is captured by the second vehicle sensor, which may be a LiDAR. A 3D point cloud representation of the scene is constructed based on the 3D point cloud data. A rigid transformation is determined between the 3D representation of the scene and the 3D point cloud representation of the scene and the alignment between the sensors is performed based at least in part on the determined rigid transformation.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. Pat. ApplicationNo. 17/013,090, filed Sep. 4, 2020, the content of which is herebyincorporated by reference in their entirety.

TECHNICAL FIELD

The present invention relates generally to sensor alignment andcalibration, and more particularly, in some embodiments, toalignment/calibration of a first vehicle sensor to a second vehiclesensor.

BACKGROUND

On-board sensors in a vehicle, such as an autonomous vehicle, supplementand bolster the vehicle’s field-of-view (FOV) by providing continuousstreams of sensor data captured from the vehicle’s surroundingenvironment. Sensor data is used in connection with a diverse range ofvehicle-based applications including, for example, blind spot detection,lane change assisting, rear-end radar for collision warning or collisionavoidance, park assisting, cross-traffic monitoring, brake assisting,emergency braking, and automated distance control. In addition, thesensor data can be used to perform a variety of machine learning tasksthat support the above-described vehicle-based applications such asobject detection, semantic segmentation, instance segmentation, and thelike.

On-board sensors can include, for example, cameras, light detection andranging (LiDAR)-based systems, radar-based systems, Global PositioningSystem (GPS) systems, sonar-based sensors, ultrasonic sensors, inertialmeasurement units (IMUs), accelerometers, gyroscopes, magnetometers, andfar infrared (FIR) sensors. Sensor data may include image data,reflected laser data, or the like. Often, combining (e.g., fusing) thesensor data from different sensors can provide a more powerful datasetthat results in better performance, more accurate calculations, etc.when performing computational tasks related to autonomous vehicleoperation. In order to successfully fuse sensor data from differentsensors, the sensors must be aligned/calibrated with one another.Discussed herein are technical solutions that address technicaldrawbacks associated with convention sensor alignment/calibrationsystems.

SUMMARY

In an example embodiment, a computer-implemented method for performingan alignment between a first vehicle sensor and a second vehicle sensoris disclosed. The computer-implemented method includes receivingtwo-dimensional (2D) data captured by the first vehicle sensor, wherethe 2D data is indicative of a scene within an environment beingtraversed by a vehicle, and constructing a three-dimensional (3D)representation of the scene based at least in part on the 2D data. Themethod further includes receiving 3D point cloud data captured by thesecond vehicle sensor, where the 3D point cloud data is indicative ofthe scene, and constructing a 3D point cloud representation of the scenebased at least in part on the 3D point cloud data. The method furtherincludes determining a rigid transformation between the 3Drepresentation of the scene and the 3D point cloud representation of thescene and performing the alignment between the first vehicle sensor andthe second vehicle sensor based at least in part on the determined rigidtransformation.

In an example embodiment, the first vehicle sensor is a camera, thesecond vehicle sensor is a LiDAR, the 2D data includes image datacaptured by the camera from a plurality of camera poses as the vehicletraverses the environment, and the 3D point cloud data includes LiDARdata points captured by a plurality of scans of the LiDAR as the vehicletraverses the environment.

In an example embodiment, determining the rigid transformation includesdetermining a transformation matrix that provides a best match betweenthe 3D representation of the scene and the 3D point cloud representationof the scene.

In an example embodiment, the computer-implemented method for performingan alignment between a first vehicle sensor and a second vehicle sensorfurther includes determining a scaling factor between the 3Drepresentation of the scene and the 3D point cloud representation of thescene, where the alignment between the first vehicle sensor and thesecond vehicle sensor is performed further based at least in part on thedetermined scaling factor.

In an example embodiment, determining the scaling factor includesreceiving a first set of 3D point cloud data points captured during afirst scan performed by the second vehicle sensor, receiving a secondset of 3D point cloud data points captured during a second scanperformed by the second vehicle sensor, and orienting the first set of3D point cloud data points to the second set of 3D point cloud datapoints.

In an example embodiment, the first scan occurs while the vehicle is ina first location corresponding to a first pose with respect to theenvironment and the second scan occurs while the vehicle is in a secondlocation corresponding to a second pose with respect to the environment,and orienting the first set of 3D point cloud data points to the secondset of 3D point cloud data points includes orienting the first set of 3Dpoint cloud data points to the second set of 3D point cloud data pointsbased at least in part a real-world orientation of the first pose withrespect to the second pose.

In an example embodiment, determining the scaling factor furtherincludes determining an actual distance between the first location andthe second location after orienting the first set of 3D point cloud datapoints to the second set of 3D point cloud data points, determining arelative distance between the first location and the second locationbased at least in part on the 3D representation of the scene constructedfrom the 2D data, and determining the scaling factor as the ratio of theactual distance to the relative distance.

In an example embodiment, a system for performing an alignment between afirst vehicle sensor and a second vehicle sensor is disclosed. Thesystem includes at least one processor and at least one memory storingcomputer-executable instructions. The at least one processor isconfigured to access the at least one memory and execute thecomputer-executable instructions to perform a set of operationsincluding receiving two-dimensional (2D) data captured by the firstvehicle sensor, where the 2D data is indicative of a scene within anenvironment being traversed by a vehicle, and constructing athree-dimensional (3D) representation of the scene based at least inpart on the 2D data. The set of operations further includes receiving 3Dpoint cloud data captured by the second vehicle sensor, where the 3Dpoint cloud data is indicative of the scene, and constructing a 3D pointcloud representation of the scene based at least in part on the 3D pointcloud data. The set of operations further includes determining a rigidtransformation between the 3D representation of the scene and the 3Dpoint cloud representation of the scene and performing the alignmentbetween the first vehicle sensor and the second vehicle sensor based atleast in part on the determined rigid transformation.

The above-described system is further configured to perform any of theoperations/functions and may include any of the additionalfeatures/aspects of example embodiments of the invention described abovein relation to example computer-implemented methods of the invention.

In an example embodiment, a computer program product for performing analignment between a first vehicle sensor and a second vehicle sensor isdisclosed. The computer program product includes a non-transitorycomputer-readable medium readable by a processing circuit. Thenon-transitory computer-readable medium stores instructions executableby the processing circuit to cause a method to be performed. The methodincludes receiving two-dimensional (2D) data captured by the firstvehicle sensor, where the 2D data is indicative of a scene within anenvironment being traversed by a vehicle, and constructing athree-dimensional (3D) representation of the scene based at least inpart on the 2D data. The method further includes receiving 3D pointcloud data captured by the second vehicle sensor, where the 3D pointcloud data is indicative of the scene, and constructing a 3D point cloudrepresentation of the scene based at least in part on the 3D point clouddata. The method further includes determining a rigid transformationbetween the 3D representation of the scene and the 3D point cloudrepresentation of the scene and performing the alignment between thefirst vehicle sensor and the second vehicle sensor based at least inpart on the determined rigid transformation

The above-described computer program product is further configured toperform any of the operations/functions and may include any of theadditional features/aspects of example embodiments of the inventiondescribed above in relation to example computer-implemented methods ofthe invention.

These and other features of the systems, methods, and non-transitorycomputer readable media disclosed herein, as well as the methods ofoperation and functions of the related elements of structure and thecombination of parts and economies of manufacture, will become moreapparent upon consideration of the following description and theappended claims with reference to the accompanying drawings, all ofwhich form a part of this specification, wherein like reference numeralsdesignate corresponding parts in the various figures. It is to beexpressly understood, however, that the drawings are for purposes ofillustration and description only and are not intended as a definitionof the limits of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain features of various embodiments of the present technology areset forth with particularity in the appended claims. A betterunderstanding of the features and advantages of the technology will beobtained by reference to the following detailed description that setsforth illustrative embodiments, in which the principles of the inventionare utilized, and the accompanying drawings of which:

FIG. 1 is an aerial view of a sensor assembly in accordance with anexample embodiment of the invention.

FIG. 2A schematically illustrates alignment/calibration of a LiDAR withrespect to a camera in accordance with an example embodiment of theinvention.

FIG. 2B schematically illustrates in more detail generating of a scalingfactor used, at least in part, to perform the alignment/calibrationillustrated in FIG. 2A in accordance with an example embodiment of theinvention.

FIG. 3 is a process flow diagram of an illustrative method foraligning/calibrating a LiDAR with respect to a camera in accordance withan example embodiment of the invention.

FIG. 4 is a process flow diagram of an illustrative method fordetermining a scaling factor that is used, at least in part, toalign/calibrate a LiDAR with respect to a camera in accordance with anexample embodiment of the invention.

FIG. 5 is a process flow diagram of an illustrative method for utilizinga statistical processing model to determine the scaling factor inaccordance with an example embodiment of the invention.

FIG. 6 is a schematic block diagram illustrating an example networkedarchitecture configured to implement example embodiments of theinvention.

DETAILED DESCRIPTION

In the following description, certain specific details are set forth inorder to provide a thorough understanding of various embodiments of theinvention. However, one skilled in the art will understand that theinvention may be practiced without these details. Moreover, whilevarious embodiments of the invention are disclosed herein, manyadaptations and modifications may be made within the scope of theinvention in accordance with the common general knowledge of thoseskilled in this art. Such modifications include the substitution ofknown equivalents for any aspect of the invention in order to achievethe same result in substantially the same way.

Unless the context requires otherwise, throughout the presentspecification and claims, the word “comprise” and variations thereof,such as, “comprises” and “comprising” are to be construed in an open,inclusive sense, that is as “including, but not limited to.” Recitationof numeric ranges of values throughout the specification is intended toserve as a shorthand notation of referring individually to each separatevalue falling within the range inclusive of the values defining therange, and each separate value is incorporated in the specification asit were individually recited herein. Additionally, the singular forms“a,” “an” and “the” include plural referents unless the context clearlydictates otherwise. The phrases “at least one of,” “at least oneselected from the group of,” or “at least one selected from the groupconsisting of,” and the like are to be interpreted in the disjunctive(e.g., not to be interpreted as at least one of A and at least one ofB).

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure or characteristicdescribed in connection with the embodiment is included in at least oneembodiment of the present invention. Thus, the appearances of thephrases “in one embodiment” or “in an embodiment” in various placesthroughout this specification are not necessarily all referring to thesame embodiment, but may be in some instances. Furthermore, theparticular features, structures, or characteristics may be combined inany suitable manner in one or more embodiments.

In general, a vehicle (e.g., an autonomous vehicle) can have a myriad ofsensors onboard the vehicle. Such sensors can be disposed on an exterioror in an interior of a vehicle and can include, without limitation,LiDAR sensors, radars, cameras, GPS receivers, sonar-based sensors,ultrasonic sensors, IMUs, accelerometers, gyroscopes, magnetometers, FIRsensors, and so forth. Such sensors play a central role in thefunctioning and operation of an autonomous vehicle. For example, LiDARscan be utilized to detect objects (e.g., other vehicles, road signs,pedestrians, buildings, etc.) in an environment around a vehicle. LiDARscan also be utilized to determine relative distances between objects inthe environment and between objects and the vehicle. As anothernon-limiting example, radars can be utilized in connection withcollision avoidance, adaptive cruise control, blind spot detection,assisted parking, and other vehicle applications. As yet anothernon-limiting example, cameras can be utilized to recognize, interpret,and/or identify objects captured in images or visual cues of theobjects. Cameras and other optical sensors can capture image data usingcharge coupled devices (CCDs), complementary metal oxide semiconductors(CMOS), or similar elements. Data collected from these sensors can beprocessed and used, as inputs, to algorithms configured to make variousautonomous driving decisions including decisions relating to when andhow much to accelerate, decelerate, change direction, or the like.

In various example embodiments of the invention, the myriad of sensorspreviously described (e.g., LiDARs, radars, cameras, etc.) providecontinuous streams of sensor data that are provided as input toalgorithms that perform complex calculations in order to facilitate amultitude of operations required for safe autonomous vehicle operationsuch as object detection, object classification, object tracking,collision avoidance, vehicle navigation, vehicle acceleration anddeceleration, and the like. Often, combining (e.g., fusing) the sensordata from different sensors can provide a more powerful dataset thatresults in better performance, more accurate calculations, etc. whenperforming computational tasks related to autonomous vehicle operation.In order to successfully fuse sensor data from different sensors, thesensors must be aligned/calibrated with one another. Sensoralignment/calibration, however, can be a time-consuming process.Further, sensor alignment/calibration between sensors that capture datacorresponding to different dimensionalities can be a particularlytime-intensive task.

Various embodiments of the invention overcome technical problemsspecifically arising in the realm of computer-based technology, and morespecifically, in the realm of autonomous vehicle technology. Inparticular, example embodiments of the invention provide technicalsolutions that improve the efficiency of sensor alignment/calibration.These technical solutions are provided in the form of systems, methods,non-transitory computer-readable media, techniques, and methodologiesfor performing an alignment between a first vehicle sensor and a secondvehicle sensor using respective sensor data captured by the sensors and3D scene representations constructed using the respective sensor data.In this manner, the sensor alignment/calibration can be achieved in amore efficient and less time-intensive manner because sensor datacaptured during vehicle operation can be used to perform the sensoralignment/calibration.

In an example embodiment, the first vehicle sensor may be a camera or acollection of cameras forming part of a sensor assembly. In an exampleembodiment, the second vehicle sensor may be a LiDAR sensor, which mayalso form part of the sensor assembly with the camera(s). The camera maybe configured to periodically capture 2D image data at a designatedframe capture rate. The 2D data may capture image data of a scene in anenvironment being traversed by a vehicle from different camera poses.The LiDAR may be configured to periodically scan the vehicle’senvironment by transmitting pulses of light at periodic intervals as theLiDAR moves along the scan path. The LiDAR sensor may be furtherconfigured to measure differences in return times and wavelengths forthe light that is reflected back to the LiDAR and generate 3D pointcloud data (a set of data points in space) representative of a targetobject that it has illuminated with light during its scan path.

In example embodiments, a camera provided, for example, on an exteriorof a vehicle such as an autonomous vehicle may capture a set of 2Dimages of a scene in an environment being traversed by the vehicle. A 3Drepresentation of the scene may then be constructed using the set ofimages. In addition, in example embodiments, a collection of 3D pointcloud data points may be captured by a LiDAR provided, for example, onan exterior of the vehicle. The 3D point cloud data may also correspondto the scene captured by the 2D image data. A 3D point cloudrepresentation of the scene may be constructed using the set of 3D pointcloud data points.

A rigid transformation may then be determined between the 3Drepresentation of the scene constructed from the 2D image data and the3D point cloud representation of the scene. The rigid transformation maybe embodied as a transformation matrix that includes both rotational andtranslational components that provide a best match between the 3Drepresentation of the scene and the 3D point cloud representation of thescene. The camera and the LiDAR can then be aligned/calibrated withrespect to one another based at least in part on the determined rigidtransformation.

In addition, in example embodiments, a scaling factor may be determinedbetween the 3D representation of the scene constructed from the 2D imagedata and the 3D point cloud representation of the scene. The scalingfactor may represent a ratio between an actual distance between objects(or portions of an object) present in the captured scene and a relativedistance between the objects (or portions of the object) in the 3Drepresentation of the scene constructed from the 2D image data. Inexample embodiments, the sensor alignment/calibration may be performedfurther based on the determined scaling factor.

By utilizing sensor data captured by vehicle sensors during vehicleoperation to align/calibrate the vehicle sensors in accordance withexample embodiments of the invention, the sensor alignment/calibrationcan be achieved in a more efficient and less time-intensive manner. Inparticular, the alignment/calibration of different vehicle sensors canbe performed simultaneously with vehicle operation without requiringseparate and distinct data collection solely for calibration purposes.As such, example embodiments of the invention provide a technicalsolution that represents a technological improvement over conventionalsensor alignment/calibration techniques.

FIG. 1 is an aerial view of a sensor assembly 104 in accordance with anexample embodiment of the invention. The sensor assembly 104 may includea variety of different types of sensors including, for example, one ormore LiDAR sensors 108 and one or more cameras 106. Although notdepicted in FIG. 1 , the sensor assembly 104 may further include othertypes of sensors such as, for example, one or more IMUs, one or more GPSreceivers, and so forth. In the example configuration depicted in FIG. 1, the LiDAR sensor 108 is centrally located on a roof of a vehicle 102and is surrounded by multiple cameras that are positionedcircumferentially around the LiDAR sensor 108. In example embodiments,the LiDAR sensor 108 may periodically rotate through a scan path duringwhich the LiDAR 108 may illuminate objects in the scanned environmentwith pulses of light and measure the differences in flight times andwavelengths for light that is reflected back to detect the presence oftarget objects, determine distances between the vehicle 102 and thetarget objects, determine distances between various target objects, andthe like. The LiDAR 108 may exhibit a horizontal scan path and/or avertical scan path.

In example embodiments, as the LiDAR 108 travels through its scan path,it may become aligned with each camera 106 of the sensor assembly at arespective particular point in time. Determining an alignment betweeneach camera 106 and the LiDAR 108 would allow the image data captured bythe cameras 106 to be matched (e.g., fused) with the LiDAR scan data tofacilitate a variety of autonomous vehicle processing tasks, such asobject recognition, for example. Described herein are techniques forperforming an alignment/calibration of the LiDAR 108 to a camera 106using respective data captured by each sensor.

FIG. 2A schematically illustrates alignment/calibration of a LiDAR withrespect to a camera in accordance with an example embodiment of theinvention. FIG. 3 is a process flow diagram of an illustrative method300 for aligning/calibrating a LiDAR with respect to a camera inaccordance with an example embodiment of the invention. FIG. 3 will bedescribed in conjunction with FIG. 2A hereinafter. FIG. 2B schematicallyillustrates in more detail determination of a scaling factor used, atleast in part, to perform the alignment/calibration illustrated in FIG.2A in accordance with an example embodiment of the invention. FIG. 4 isa process flow diagram of an illustrative method 400 for determining ascaling factor that is used, at least in part, to align/calibrate aLiDAR with respect to a camera in accordance with an example embodimentof the invention. FIG. 4 will be described in conjunction with FIG. 2Blater in this disclosure.

Each operation of the method 300 and/or the method 400 can be performedby one or more of the engines/program modules depicted in FIGS. 2A, 2B,or 5 , whose operation will be described in more detail hereinafter.These engines/program modules can be implemented in any combination ofhardware, software, and/or firmware. In certain example embodiments, oneor more of these engines/program modules can be implemented, at least inpart, as software and/or firmware modules that includecomputer-executable instructions that when executed by a processingcircuit cause one or more operations to be performed. In exampleembodiments, these engines/program modules may be customizedcomputer-executable logic implemented within a customized computingmachine such as a customized FPGA or ASIC. A system or device describedherein as being configured to implement example embodiments of theinvention can include one or more processing circuits, each of which caninclude one or more processing units or cores. Computer-executableinstructions can include computer-executable program code that whenexecuted by a processing core can cause input data contained in orreferenced by the computer-executable program code to be accessed andprocessed by the processing core to yield output data.

Referring first to FIG. 3 in conjunction with FIG. 2A, at block 302 ofthe method 300, a 3D scene representation generation engine 214 mayreceive image data 210 captured by a camera 204. The camera 204 may be aparticular implementation of a camera 106 depicted in FIG. 1 . Whileexample embodiments are described herein with respect to aligning afirst vehicle sensor (e.g., the camera 204) with a LiDAR 202 (which maybe a particular implementation of the LiDAR 108 depicted in FIG. 1 ), itshould be appreciated that techniques described herein can be used toalign/calibrate the LiDAR 202 with any number of cameras 204.

In an example embodiment, the image data 210 received at block 302 mayinclude a set of images of a scene 208 captured by the camera 204 of anenvironment surrounding a vehicle as the vehicle is traversing theenvironment. The set of images may be captured from different cameraposes as the vehicle traverses the environment. That is, even though thecamera 204 may be fixedly mounted to the vehicle, as the vehiclemaneuvers through the environment, the camera 204 may capture the imagedata 210 from a variety of different camera poses.

At block 304 of the method 300, the 3D scene representation generationengine 214 may construct a 3D image representation of the scene 216using the set of images contained in the image data 210. In some exampleembodiments, the 3D image representation of the scene 216 may beconstructed by stitching together images in the set of images that arecaptured from different camera poses. The images may be stitchedtogether based on the real-world orientation of the camera poses toproduce the 3D image representation of the scene 216.

At block 306 of the method 300, the 3D scene representation generationengine 214 may receive point cloud data 212 from the LiDAR 202. Inexample embodiments, the point cloud data 212 may include a collectionof data points captured by LiDAR 206 during one or more scan paths 206.In example embodiments, the collection of 3D LiDAR data points may berepresentative of the scene 208 captured by the LiDAR’s scan paths 206as the vehicle traverses the environment.

At block 308 of the method 300, the 3D scene representation generationengine 214 may generate a 3D point cloud representation of the scene 218using the collection of LiDAR data points. The 3D point cloudrepresentation of the scene 218 may include sets of LiDAR data pointscaptured during different scan paths of the LiDAR 202 at orientationswith respect to the scene 208 within the environment being traversed bythe vehicle. While the LiDAR data points from a single scan of the LiDAR202 may be a 3D collection of data points, they may not fully representthe 3D scene 208 being captured. As such, the engine 214 may construct afull 3D representation of the scene 218 using 3D LiDAR data pointscaptured from multiple LiDAR scans.

At block 310 of the method 300, a rigid transformation determinationengine 220 may determine a rigid transformation 222 that provides a bestmatch between the 3D image representation of the scene 216 and the 3Dpoint cloud representation of the scene 218. In some exampleembodiments, the rigid transformation may be a transformation matrixthat includes rotational and translational components that define atransformation from a coordinate system of the LiDAR 202 to a coordinatesystem of the camera 206, and vice versa. In example embodiments, therigid transformation 222 can be used align/calibrate the LiDAR 202 andthe camera 206 such that 3D point cloud data generated by the LiDAR 202can be oriented in the real-world with image data captured by the camera206, thereby allowing the 2D image data and the 3D point cloud data tobe fused.

At block 312 of the method 300, a scaling factor determination engine226 may determine a scaling factor 228 between the 3D point cloudrepresentation of the scene 218 and the 3D representation of the scene216 reconstructed from the set of images contained in the image data210. Generation of the scaling factor 228 will be described in moredetail hereinafter in reference to FIGS. 2B and 4 .

Finally, at block 314 of the method 300, an alignment/calibration engine224 may perform an alignment/calibration between the LiDAR 202 and thecamera 206 based at least in part on the rigid transformation 220 andthe scaling factor 228. Once the LiDAR 202 and the camera 206 arealigned/calibrated with respect one another, the respective data theycaptured can be easily fused and used to perform various autonomousvehicle processing tasks.

The process for generating the scaling factor 228 will now be describedin reference to FIGS. 2B and 4 . In example embodiments, determining thescaling factor 228 may be necessary because the scale of objectscontained in the 3D image representation of the scene 216 may notrepresent an accurate real-world scale due to the scale of the imagedata 210 being different from the real-world scale of objects.

Referring now to FIG. 4 in conjunction with FIG. 2B, at block 402 of themethod 400, the scaling factor determination engine 226 may receive afirst set of point cloud data points 230 from a first scan of the LiDAR202. In example embodiments, the first LiDAR scan may occur while avehicle is in a first location corresponding to a first pose of theLiDAR 202 with respect to a surrounding environment.

At block 404 of the method 400, the scaling factor determination engine226 may receive a second set of point cloud data points 232 from asecond scan of the LiDAR 202. In example embodiments, the second LiDARscan may occur while the vehicle is in a second location correspondingto a second pose of the LiDAR 202 with respect to the surroundingenvironment.

At block 406 of the method 400, an orientation module 234 of the scalingfactor determination engine 226 may orient the first set of point clouddata points 230 to the second set of point cloud data points 232 basedon a real-world orientation of the first pose with respect to the secondpose. As previously described, the first pose may correspond to a firstorientation of the LiDAR 202 with respect to the sensed environment whenthe vehicle is at the first location. Similarly, the second pose maycorrespond to a second orientation of the LiDAR 202 with respect to thesensed environment when the vehicle is in the second location.

At block 408 of the method 400, a distance module 236 of the scalingfactor determination engine 226 may determine an actual distance betweenthe first location and the second location based on the oriented sets ofpoint cloud data points. At block 410 of the method 400, the distancemodule 236 may determine a relative distance between the first locationand the second location based at least in part on the 3D imagerepresentation of the scene 216 reconstructed from image data 210.Finally, at block 412 of the method 400, the scaling factordetermination engine 226 may determine the scaling factor 228 as theratio of the actual distance determined at block 408 to the relativedistance determined at block 410. As previously described, the scalingfactor 228 may then be used to in conjunction with the rigidtransformation 222 to perform the alignment/calibration of the LiDAR 202and the camera 206.

Referring now to FIG. 5 , an illustrative method 500 for determining thescaling factor 228 is begins at block 502, where the scaling factordetermination engine 226 determines a set of multiple scaling factorsusing multiple point cloud datasets. The point cloud datasets mayinclude multiple sets of LiDAR data points corresponding to multipleLiDAR scans from different vehicle poses.

At block 504 of the method 500, the scaling factor determination engine226 may determine a statistical model to evaluate the set of scalingfactors. In some example embodiments, the statistical model may be alinear regression model. In some example embodiments, the statisticalmodel may be based on an assumption that the set of scaling factors willhave a Gaussian distribution. In some example embodiments, thestatistical model may employ a Monte Carlo simulation.

At block 506 of the method 500, the scaling factor determination engine226 may apply the determined statistical model to the set of scalingfactors to thereby filter out one or more outliers from the set ofscaling factors. For instance, the engine 226 may identify and filterout outliers based on an assumed Gaussian distribution for the set ofscaling factors. The outliers may be identified as those points (i.e.,scaling factors) that fall at least a threshold number of standarddeviation(s) from a mean.

At block 508 of the method 500, engine 226 may determine a statisticalquantity from the filtered scaling factors. In some example embodiments,the statistical quantity may be a mean of the scaling factors thatremain after the outliers are filtered out. In other exampleembodiments, the statistical quantity may be the root mean squared ofthe set of filtered scaling factors. In yet other example embodiments,the statistical quantity may be the mode, the median, or some otherstatistical quantity derivable from the filtered set of scaling factors.

At block 510 of the method 500, a sensor alignment/calibration may beperformed based at least in part on the determined statistical quantity.More specifically, the alignment/calibration engine 224 mayalign/calibrate a first vehicle sensor (e.g., the LiDAR 202) to a secondvehicle sensor (e.g., the camera 204) using the rigid transformation 222generated by the rigid transformation determination engine 220 as wellas the statistical quantity determined at block 508, which can be usedas scaling factor 228. It should be appreciated that in some exampleembodiments, a similar process as method 500 may be executed todetermine the rigid transformation 222. In particular, multiplecandidate rigid transformations may be determined, a statistical modelmay be used to filter outliers from the set of candidate rigidtransformations, a statistical quantity may be determined from thefiltered set of candidate rigid transformations, and the determinedstatistical quantity may be selected as the rigid transformation 222.

Hardware Implementation

FIG. 6 is a schematic block diagram illustrating an example networkedarchitecture 600 configured to implement example embodiments of theinvention. The networked architecture 600 can include one or morespecial-purpose computing devices 602 communicatively coupled via one ormore networks 606 to various sensors 604. The sensors 604 may includeany of the example types of on-board vehicle sensors previouslydescribed including, without limitation, LiDAR sensors, radars, cameras,GPS receivers, sonar-based sensors, ultrasonic sensors, IMUs,accelerometers, gyroscopes, magnetometers, FIR sensors, and so forth. Inexample embodiments, the sensors 604 may include on-board sensorsprovided on an exterior or in an interior of a vehicle such as anautonomous vehicle. The special-purpose computing device(s) 602 mayinclude devices that are integrated with a vehicle and may receivesensor data from the sensors 604 via a local network connection (e.g.,WiFi, Bluetooth, Dedicated Short Range Communication (DSRC), or thelike). In other example embodiments, the special-purpose computingdevice(s) 602 may be provided remotely from a vehicle and may receivethe sensor data from the sensors 604 via one or more long-rangenetworks.

The special-purpose computing device(s) 602 may be hard-wired to performthe techniques; may include circuitry or digital electronic devices suchas one or more ASICs or FPGAs that are persistently programmed toperform the techniques; and/or may include one or more hardwareprocessors programmed to perform the techniques pursuant to programinstructions in firmware, memory, other storage, or a combinationthereof. The special-purpose computing device(s) 602 may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing device(s) 602may be desktop computer systems, server computer systems, portablecomputer systems, handheld devices, networking devices or any otherdevice or combination of devices that incorporate hard-wired and/orprogrammed logic to implement the techniques.

The special-purpose computing device(s) may be generally controlled andcoordinated by operating system software 620, such as iOS, Android,Chrome OS, Windows XP, Windows Vista, Windows 4, Windows 8, WindowsServer, Windows CE, Unix, Linux, SunOS, Solaris, iOS, Blackberry OS,VxWorks, or other compatible operating systems. In other embodiments,the computing device(s) 602 may be controlled by a proprietary operatingsystem. The operating system software 620 may control and schedulecomputer processes for execution; perform memory management; providefile system, networking, and I/O services; and provide user interfacefunctionality, such as a graphical user interface (“GUI”).

While the computing device(s) 602 and/or the sensors 604 may bedescribed herein in the singular, it should be appreciated that multipleinstances of any such component can be provided and functionalitydescribed in connection any particular component can be distributedacross multiple instances of such a component. In certain exampleembodiments, functionality described herein in connection with any givencomponent of the architecture 600 can be distributed among multiplecomponents of the architecture 600. For example, at least a portion offunctionality described as being provided by a computing device 602 maybe distributed among multiple such computing devices 602.

The network(s) 606 can include, but are not limited to, any one or moredifferent types of communications networks such as, for example, cablenetworks, public networks (e.g., the Internet), private networks (e.g.,frame-relay networks), wireless networks, cellular networks, telephonenetworks (e.g., a public switched telephone network), or any othersuitable private or public packet-switched or circuit-switched networks.The network(s) 606 can have any suitable communication range associatedtherewith and can include, for example, global networks (e.g., theInternet), metropolitan area networks (MANs), wide area networks (WANs),local area networks (LANs), or personal area networks (PANs). Inaddition, the network(s) 606 can include communication links andassociated networking devices (e.g., link-layer switches, routers, etc.)for transmitting network traffic over any suitable type of mediumincluding, but not limited to, coaxial cable, twisted-pair wire (e.g.,twisted-pair copper wire), optical fiber, a hybrid fiber-coaxial (HFC)medium, a microwave medium, a radio frequency communication medium, asatellite communication medium, or any combination thereof.

In an illustrative configuration, the computing device 602 can includeone or more processors (processor(s)) 608, one or more memory devices610 (generically referred to herein as memory 610), one or moreinput/output (“I/O”) interface(s) 612, one or more network interfaces614, and data storage 618. The computing device 602 can further includeone or more buses 616 that functionally couple various components of thecomputing device 602. The data storage may store one or more engines,program modules, components, or the like including, without limitation,a 3D scene representation generation engine 624, a scaling factordetermination engine 626, a rigid transformation determination engine628, and an alignment/calibration engine 630. Each of theengines/components depicted in FIG. 6 may include logic for performingany of the processes or tasks described earlier in connection withcorrespondingly named engines/components. In certain exampleembodiments, any of the depicted engines/components may be implementedin hard-wired circuitry within digital electronic devices such as one ormore ASICs or FPGAs that are persistently programmed to performcorresponding techniques.

The bus(es) 616 can include at least one of a system bus, a memory bus,an address bus, or a message bus, and can permit the exchange ofinformation (e.g., data (including computer-executable code), signaling,etc.) between various components of the computing device 602. Thebus(es) 616 can include, without limitation, a memory bus or a memorycontroller, a peripheral bus, an accelerated graphics port, and soforth. The bus(es) 616 can be associated with any suitable busarchitecture including, without limitation, an Industry StandardArchitecture (ISA), a Micro Channel Architecture (MCA), an Enhanced ISA(EISA), a Video Electronics Standards Association (VESA) architecture,an Accelerated Graphics Port (AGP) architecture, a Peripheral ComponentInterconnects (PCI) architecture, a PCI-Express architecture, a PersonalComputer Memory Card International Association (PCMCIA) architecture, aUniversal Serial Bus (USB) architecture, and so forth.

The memory 610 can include volatile memory (memory that maintains itsstate when supplied with power) such as random access memory (RAM)and/or non-volatile memory (memory that maintains its state even whennot supplied with power) such as read-only memory (ROM), flash memory,ferroelectric RAM (FRAM), and so forth. Persistent data storage, as thatterm is used herein, can include non-volatile memory. In certain exampleembodiments, volatile memory can enable faster read/write access thannon-volatile memory. However, in certain other example embodiments,certain types of non-volatile memory (e.g., FRAM) can enable fasterread/write access than certain types of volatile memory.

In various implementations, the memory 610 can include multipledifferent types of memory such as various types of static random accessmemory (SRAM), various types of dynamic random access memory (DRAM),various types of unalterable ROM, and/or writeable variants of ROM suchas electrically erasable programmable read-only memory (EEPROM), flashmemory, and so forth. The memory 610 can include main memory as well asvarious forms of cache memory such as instruction cache(s), datacache(s), translation lookaside buffer(s) (TLBs), and so forth. Further,cache memory such as a data cache can be a multi-level cache organizedas a hierarchy of one or more cache levels (L1, L2, etc.). In exampleembodiments, the memory 610 may include the data storage 106(1)-106(P)and/or the data storage 120 depicted in FIG. 1 . Alternatively, the datastorage 106(1)-106(P) may be hard disk storage forming part of the datastorage 618 and/or the data storage 120 may be a form of RAM or cachememory that is provided as part of the FOV semantics computing machine624 itself.

The data storage 618 can include removable storage and/or non-removablestorage including, but not limited to, magnetic storage, optical diskstorage, and/or tape storage. The data storage 618 can providenon-volatile storage of computer-executable instructions and other data.The memory 610 and the data storage 618, removable and/or non-removable,are examples of computer-readable storage media (CRSM) as that term isused herein. The data storage 618 can store computer-executable code,instructions, or the like that can be loadable into the memory 610 andexecutable by the processor(s) 608 to cause the processor(s) 608 toperform or initiate various operations. The data storage 618 canadditionally store data that can be copied to memory 610 for use by theprocessor(s) 608 during the execution of the computer-executableinstructions. Moreover, output data generated as a result of executionof the computer-executable instructions by the processor(s) 608 can bestored initially in memory 610 and can ultimately be copied to datastorage 618 for non-volatile storage.

More specifically, the data storage 618 can store one or more operatingsystems (O/S) 620 and one or more database management systems (DBMS) 622configured to access the memory 610 and/or one or more externaldatastore(s) (not depicted) potentially via one or more of the networks606. In addition, the data storage 618 may further store one or moreprogram modules, applications, engines, computer-executable code,scripts, or the like. For instance, any of the engines/componentsdepicted in FIG. 6 may be implemented as software and/or firmware thatincludes computer-executable instructions (e.g., computer-executableprogram code) loadable into the memory 610 for execution by one or moreof the processor(s) 608 to perform any of the techniques describedherein.

Although not depicted in FIG. 6 , the data storage 618 can further storevarious types of data utilized by engines/components of the computingdevice 602. Such data may include, without limitation, sensor data,feedback data including historical sensor operational data, initialparameter data, or the like. Any data stored in the data storage 618 canbe loaded into the memory 610 for use by the processor(s) 608 inexecuting computer-executable program code. In addition, any data storedin the data storage 618 can potentially be stored in one or moreexternal datastores that are accessible via the DBMS 622 and loadableinto the memory 610 for use by the processor(s) 608 in executingcomputer-executable instructions/program code.

The processor(s) 608 can be configured to access the memory 610 andexecute computer-executable instructions/program code loaded therein.For example, the processor(s) 608 can be configured to executecomputer-executable instructions/program code of the variousengines/components of the FOV semantics computing machine 624 to causeor facilitate various operations to be performed in accordance with oneor more embodiments of the invention. The processor(s) 608 can includeany suitable processing unit capable of accepting data as input,processing the input data in accordance with stored computer-executableinstructions, and generating output data. The processor(s) 608 caninclude any type of suitable processing unit including, but not limitedto, a central processing unit, a microprocessor, a Reduced InstructionSet Computer (RISC) microprocessor, a Complex Instruction Set Computer(CISC) microprocessor, a microcontroller, an Application SpecificIntegrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), aSystem-on-a-Chip (SoC), a digital signal processor (DSP), and so forth.Further, the processor(s) 608 can have any suitable microarchitecturedesign that includes any number of constituent components such as, forexample, registers, multiplexers, arithmetic logic units, cachecontrollers for controlling read/write operations to cache memory,branch predictors, or the like. The microarchitecture design of theprocessor(s) 608 can be made capable of supporting any of a variety ofinstruction sets.

Referring now to other illustrative components depicted as being storedin the data storage 618, the O/S 620 can be loaded from the data storage618 into the memory 610 and can provide an interface between otherapplication software executing on the computing device 602 and hardwareresources of the computing device 602. More specifically, the O/S 620can include a set of computer-executable instructions for managinghardware resources of the computing device 602 and for providing commonservices to other application programs. In certain example embodiments,the O/S 620 can include or otherwise control execution of one or more ofthe engines/program modules stored in the data storage 618. The O/S 620can include any operating system now known or which can be developed inthe future including, but not limited to, any server operating system,any mainframe operating system, or any other proprietary ornon-proprietary operating system.

The DBMS 622 can be loaded into the memory 610 and can supportfunctionality for accessing, retrieving, storing, and/or manipulatingdata stored in the memory 610, data stored in the data storage 618,and/or data stored in external datastore(s). The DBMS 622 can use any ofa variety of database models (e.g., relational model, object model,etc.) and can support any of a variety of query languages. The DBMS 622can access data represented in one or more data schemas and stored inany suitable data repository. Datastore(s) that may be accessible by thecomputing device 602 via the DBMS 622, can include, but are not limitedto, databases (e.g., relational, object-oriented, etc.), file systems,flat files, distributed datastores in which data is stored on more thanone node of a computer network, peer-to-peer network datastores, or thelike.

Referring now to other illustrative components of the computing device602, the input/output (I/O) interface(s) 612 can facilitate the receiptof input information by the computing device 602 from one or more I/Odevices as well as the output of information from the computing device602 to the one or more I/O devices. The I/O devices can include any of avariety of components such as a display or display screen having a touchsurface or touchscreen; an audio output device for producing sound, suchas a speaker; an audio capture device, such as a microphone; an imageand/or video capture device, such as a camera; a haptic unit; and soforth. Any of these components can be integrated into the computingdevice 602 or can be separate therefrom. The I/O devices can furtherinclude, for example, any number of peripheral devices such as datastorage devices, printing devices, and so forth.

The I/O interface(s) 612 can also include an interface for an externalperipheral device connection such as universal serial bus (USB),FireWire, Thunderbolt, Ethernet port or other connection protocol thatcan connect to one or more networks. The I/O interface(s) 612 can alsoinclude a connection to one or more antennas to connect to one or morenetworks via a wireless local area network (WLAN) (such as Wi-Fi) radio,Bluetooth, and/or a wireless network radio, such as a radio capable ofcommunication with a wireless communication network such as a Long TermEvolution (LTE) network, WiMAX network, 3G network, etc.

The computing device 602 can further include one or more networkinterfaces 614 via which the computing device 602 can communicate withany of a variety of other systems, platforms, networks, devices, and soforth. The network interface(s) 614 can enable communication, forexample, with the sensors 604 and/or one or more other devices via oneor more of the network(s) 606. In example embodiments, the networkinterface(s) 614 provide a two-way data communication coupling to one ormore network links that are connected to one or more of the network(s)606. For example, the network interface(s) 614 may include an integratedservices digital network (ISDN) card, a cable modem, a satellite modem,or a modem to provide a data communication connection to a correspondingtype of telephone line. As another non-limiting example, the networkinterface(s) 614 may include a local area network (LAN) card to providea data communication connection to a compatible LAN (or a wide areanetwork (WAN) component to communicate with a WAN). Wireless links mayalso be implemented. In any such implementation, the networkinterface(s) 614 may send and receive electrical, electromagnetic, oroptical signals that carry digital data streams representing varioustypes of information.

A network link typically provides data communication through one or morenetworks to other data devices. For example, a network link may providea connection through a local network to a host computer or to dataequipment operated by an Internet Service Provider (ISP). The ISP, inturn, may provide data communication services through the world widepacket data communication network now commonly referred to as the“Internet”. Local networks and the Internet both use electrical,electromagnetic, or optical signals that carry digital data streams. Thesignals through the various network(s) 604 and the signals on networklinks and through the network interface(s) 614, which carry the digitaldata to and from the computing device 602, are example forms oftransmission media. In example embodiments, the computing device 602 cansend messages and receive data, including program code, through thenetwork(s) 606, network links, and network interface(s) 614. Forinstance, in the Internet example, a server might transmit a requestedcode for an application program through the Internet, the ISP, a localnetwork, and a network interface 614. The received code may be executedby a processor 608 as it is received, and/or stored in the data storage618, or other non-volatile storage for later execution.

It should be appreciated that the engines depicted in FIG. 6 as part ofthe computing device 602 are merely illustrative and not exhaustive. Inparticular, functionality can be modularized in any suitable manner suchthat processing described as being supported by any particular enginecan alternatively be distributed across multiple engines, programmodules, components, or the like, or performed by a different engine,program module, component, or the like. Further, one or more depictedengines may or may not be present in certain embodiments, while in otherembodiments, additional engines not depicted can be present and cansupport at least a portion of the described functionality and/oradditional functionality. In addition, various engine(s), programmodule(s), script(s), plug-in(s), Application Programming Interface(s)(API(s)), or any other suitable computer-executable code hosted locallyon the computing device 602 and/or hosted on other computing device(s)(e.g., 602) accessible via one or more of the network(s) 602, can beprovided to support functionality provided by the engines depicted inFIG. 6 and/or additional or alternate functionality. In addition,engines that support functionality described herein can be implemented,at least partially, in hardware and/or firmware and can be executableacross any number of computing devices 602 in accordance with anysuitable computing model such as, for example, a client-server model, apeer-to-peer model, and so forth.

It should further be appreciated that the computing device 602 caninclude alternate and/or additional hardware, software, and/or firmwarecomponents beyond those described or depicted without departing from thescope of the invention. More particularly, it should be appreciated thatsoftware, firmware, and/or hardware components depicted as forming partof the computing device 602 are merely illustrative and that somecomponents may or may not be present or additional components may beprovided in various embodiments. It should further be appreciated thateach of the engines depicted and described represent, in variousembodiments, a logical partitioning of supported functionality. Thislogical partitioning is depicted for ease of explanation of thefunctionality and may or may not be representative of the structure ofsoftware, hardware, and/or firmware for implementing the functionality.

In general, the terms engine, program module, or the like, as usedherein, refer to logic embodied in hardware, firmware, and/or circuitry,or to a collection of software instructions, possibly having entry andexit points, written in a programming language, such as, for example,Java, C or C++. A software engine/module may be compiled and linked intoan executable program, installed in a dynamic link library, or may bewritten in an interpreted programming language such as, for example,BASIC, Perl, or Python. It will be appreciated that softwareengines/modules may be callable from other engines/modules or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software engines/modules configured for execution oncomputing devices may be provided on a computer readable medium, such asa compact disc, digital video disc, flash drive, magnetic disc, or anyother tangible medium, or as a digital download (and may be originallystored in a compressed or installable format that requires installation,decompression or decryption prior to execution). Such software code maybe stored, partially or fully, on a memory device of the executingcomputing device, for execution by the computing device. “Open source”software refers to source code that can be distributed as source codeand/or in compiled form, with a well-publicized and indexed means ofobtaining the source, and optionally with a license that allowsmodifications and derived works. Software instructions may be embeddedin firmware and stored, for example, on flash memory such as erasableprogrammable read-only memory (EPROM). It will be further appreciatedthat hardware modules/engines may include connected logic units, such asgates and flip-flops, and/or may be further include programmable units,such as programmable gate arrays or processors.

Example embodiments are described herein as including engines or programmodules. Such engines/program modules may constitute either softwareengines (e.g., code embodied on a machine-readable medium) or hardwareengines. A “hardware engine” is a tangible unit capable of performingcertain operations and may be configured or arranged in a certainphysical manner. In various example embodiments, one or more computersystems (e.g., a standalone computer system, a client computer system,or a server computer system) or one or more hardware engines of acomputer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware engine that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware engine may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware engine may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware engine may be a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an application specific integratedcircuit (ASIC). A hardware engine may also include programmable logic orcircuitry that is temporarily configured by software to perform certainoperations. For example, a hardware engine may include a general-purposeprocessor or other programmable processor configured by software, inwhich case, the configured processor becomes a specific machine uniquelytailored to perform the configured functions and no longer constitutegeneral-purpose processors. It will be appreciated that the decision toimplement a hardware engine mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the term “engine” or “program module” should be understoodto encompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware engines are temporarily configured (e.g., programmed),each of the hardware engines need not be configured or instantiated atany one instance in time. For example, where a hardware engine includesa general-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware engines) at different times. Softwareaccordingly can configure a particular processor or processors, forexample, to constitute a particular hardware engine at a given instanceof time and to constitute a different hardware engine at a differentinstance of time.

Hardware engines can provide information to, and receive informationfrom, other hardware engines. Accordingly, the described hardwareengines may be regarded as being communicatively coupled. Where multiplehardware engines exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware engines. In embodiments inwhich multiple hardware engines are configured or instantiated atdifferent times, communications between such hardware engines may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware engines have access.For example, one hardware engine may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware engine may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware engines may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute an implementation of ahardware engine. Similarly, the methods described herein may be at leastpartially processor-implemented, with a particular processor orprocessors being an example of hardware. Moreover, the one or moreprocessors may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines includingprocessors), with these operations being accessible via a network (e.g.,the Internet) and via one or more appropriate interfaces (e.g., an API).

The performance of certain of the operations of example methodsdescribed herein may be distributed among multiple processors, not onlyresiding within a single machine, but deployed across a number ofmachines. In some example embodiments, the processors may be located ina single geographic location (e.g., within a home environment, an officeenvironment, or a server farm). In other example embodiments, theprocessors may be distributed across a number of geographic locations.

The present invention may be implemented as a system, a method, and/or acomputer program product. The computer program product may include acomputer readable storage medium (or media) having computer readableprogram instructions embodied thereon for causing a processor to carryout aspects of the present invention.

The computer readable storage medium is a form of non-transitory media,as that term is used herein, and can be any tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. The computer readable storage medium, and non-transitorymedia more generally, may include non-volatile media and/or volatilemedia. A non-exhaustive list of more specific examples of a computerreadable storage medium includes the following: a portable computerdiskette such as a floppy disk or a flexible disk; a hard disk; a randomaccess memory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), a static random access memory(SRAM), or any other memory chip or cartridge; a portable compact discread-only memory (CD-ROM); a digital versatile disk (DVD); a memorystick; a solid state drive; magnetic tape or any other magnetic datastorage medium; a mechanically encoded device such as punch-cards orraised structures in a groove having instructions recorded thereon orany physical medium with patterns of holes; any networked versions ofthe same; and any suitable combination of the foregoing.

Non-transitory media is distinct from transmission media, and thus, acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire. Non-transitory media, however, can operate inconjunction with transmission media. In particular, transmission mediamay participate in transferring information between non-transitorymedia. For example, transmission media can include coaxial cables,copper wire, and/or fiber optics, including the wires that include atleast some of the bus(es) 602. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network(LAN), a wide area network (WAN), and/or a wireless network. The networkmay include copper transmission cables, optical transmission fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user’scomputer, partly on the user’s computer, as a stand-alone softwarepackage, partly on the user’s computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user’s computer through anytype of network, including a LAN or a WAN, or the connection may be madeto an external computer (for example, through the Internet using anInternet Service Provider (ISP)). In some embodiments, electroniccircuitry including, for example, programmable logic circuitry, FPGAs,or programmable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions may also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein includes an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The various features and processes described above may be usedindependently of one another or may be combined in various ways. Allpossible combinations and subcombinations are intended to fall withinthe scope of the invention. In addition, certain method or processblocks may be omitted in some implementations. The methods and processesdescribed herein are also not limited to any particular sequence, andthe blocks or states relating thereto can be performed in othersequences that are appropriate. For example, described blocks or statesmay be performed in an order other than that specifically disclosed ormultiple blocks or states may be combined in a single block or state.The example blocks or states may be performed in serial, in parallel, orin some other manner. Blocks or states may be added to or removed fromthe disclosed example embodiments. The example systems and componentsdescribed herein may be configured differently than described. Forexample, elements may be added to, removed from, or rearranged comparedto the disclosed example embodiments.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which includes one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed partially, substantially, or entirelyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved.

It should be emphasized that many variations and modifications may bemade to the above-described embodiments, the elements of which are to beunderstood as being among other example embodiments of the invention.All such modifications and variations are intended to be included hereinwithin the scope of the invention. While example embodiments of theinvention may be referred to herein, individually or collectively, bythe term “invention,” this is merely for convenience and does not limitthe scope of the invention to any single disclosure or concept if morethan one is, in fact, disclosed. The foregoing description detailscertain embodiments of the invention. It will be appreciated, however,that no matter how detailed the foregoing appears in text, the inventioncan be practiced in many ways. It should be noted that the use ofparticular terminology when describing certain features or aspects ofthe invention should not be taken to imply that the terminology is beingre-defined herein to be restricted to including any specificcharacteristics of the features or aspects of the invention with whichthat terminology is associated.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of the invention. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

Although the invention(s) have been described in detail for the purposeof illustration based on what is currently considered to be the mostpractical and preferred implementations, it is to be understood thatsuch detail is solely for that purpose and that the invention is notlimited to the disclosed implementations, but, on the contrary, isintended to cover modifications and equivalent arrangements that arewithin the spirit and scope of the appended claims. For example, it isto be understood that the present invention contemplates that, to theextent possible, one or more features of any embodiment can be combinedwith one or more features of any other embodiment.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, program modules, engines, and/or datastores are somewhatarbitrary, and particular operations are illustrated in a context ofspecific illustrative configurations. Other allocations of functionalityare envisioned and may fall within a scope of various embodiments of theinvention. In general, structures and functionality presented asseparate resources in the example configurations may be implemented as acombined structure or resource. Similarly, structures and functionalitypresented as a single resource may be implemented as separate resources.These and other variations, modifications, additions, and improvementsfall within a scope of embodiments of the invention as represented bythe appended claims. The specification and drawings are, accordingly, tobe regarded in an illustrative rather than a restrictive sense.

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment. Inaddition, it should be appreciated that any operation, element,component, data, or the like described herein as being based on anotheroperation, element, component, data, or the like can be additionallybased on one or more other operations, elements, components, data, orthe like. Accordingly, the phrase “based on,” or variants thereof,should be interpreted as “based at least in part on.”

What is claimed is:
 1. A computer-implemented method for performing analignment between a first vehicle sensor and a second vehicle sensor,the method comprising: receiving two-dimensional (2D) data captured bythe first vehicle sensor, the 2D data indicative of a scene within anenvironment being traversed by a vehicle; constructing athree-dimensional (3D) representation of the scene based at least inpart on the 2D data; receiving 3D point cloud data captured by thesecond vehicle sensor, the 3D point cloud data indicative of the scene;constructing a 3D point cloud representation of the scene based at leastin part on the 3D point cloud data; determining a scaling factor betweenthe 3D representation of the scene and the 3D point cloud representationof the scene, wherein the determining of the scaling factor is based ona linear regression model, a Gaussian distribution, or a Monte Carlosimulation; and performing the alignment between the first vehiclesensor and the second vehicle sensor based at least in part on thedetermined scaling factor.
 2. The computer-implemented method of claim1, wherein the determining of the scaling factor comprises filtering outone or more outliers from a set of scaling factors based on the linearregression model, the Gaussian distribution, or the Monte Carlosimulation.
 3. The computer-implemented method of claim 1, furthercomprising, in response to filtering out the one or more outliers,determining a statistical quantity; and performing the alignment basedon the determined statistical quantity.
 4. The computer-implementedmethod of claim 3, wherein the statistical quantity comprises a mean,median, or a mode.
 5. The computer-implemented method of claim 1,wherein the performing of the alignment is further based on adetermination of a rigid transformation selected from candidate rigidtransformations.
 6. The computer-implemented method of claim 5, furthercomprising filtering outliers from the candidate rigid transformations.7. The computer-implemented method of claim 2, wherein the determiningof the scaling factor is based on an assumption of the set of thescaling factors being based on a Gaussian distribution.
 8. A system forperforming an alignment between a first vehicle sensor and a secondvehicle sensor, the system comprising: at least one processor; and atleast one memory storing computer-executable instructions, wherein theat least one processor is configured to access the at least one memoryand execute the computer-executable instructions to: receivetwo-dimensional (2D) data captured by the first vehicle sensor, the 2Ddata indicative of a scene within an environment being traversed by avehicle; construct a three-dimensional (3D) representation of the scenebased at least in part on the 2D data; receive 3D point cloud datacaptured by the second vehicle sensor, the 3D point cloud dataindicative of the scene; construct a 3D point cloud representation ofthe scene based at least in part on the 3D point cloud data; determine ascaling factor between the 3D representation of the scene and the 3Dpoint cloud representation of the scene, wherein the determining of thescaling factor is based on a linear regression model, a Gaussiandistribution, or a Monte Carlo simulation; and perform the alignmentbetween the first vehicle sensor and the second vehicle sensor based atleast in part on the determined scaling factor.
 9. The system of claim8, wherein the determining of the scaling factor comprises filtering outone or more outliers from a set of scaling factors based on the linearregression model, the Gaussian distribution, or the Monte Carlosimulation.
 10. The system of claim 8, wherein the at least oneprocessor is configured to, in response to filtering out the one or moreoutliers, determine a statistical quantity; and perform the alignmentbased on the determined statistical quantity.
 11. The system of claim10, wherein the statistical quantity comprises a mean, median, or amode.
 12. The system of claim 8, wherein the performing of the alignmentis further based on a determination of a rigid transformation selectedfrom candidate rigid transformations.
 13. The system of claim 12,wherein the at least one processor is configured to filter outliers fromthe candidate rigid transformations.
 14. The system of claim 9, whereinthe determining of the scaling factor is based on an assumption of theset of the scaling factors being based on a Gaussian distribution.
 15. Acomputer program product for performing an alignment between a firstvehicle sensor and a second vehicle sensor, the computer program productcomprising a non-transitory computer-readable medium storingcomputer-executable instructions that, responsive to execution by aprocessing circuit cause a method to be performed, the methodcomprising: receiving two-dimensional (2D) data captured by the firstvehicle sensor, the 2D data indicative of a scene within an environmentbeing traversed by a vehicle; constructing a three-dimensional (3D)representation of the scene based at least in part on the 2D data;receiving 3D point cloud data captured by the second vehicle sensor, the3D point cloud data indicative of the scene; constructing a 3D pointcloud representation of the scene based at least in part on the 3D pointcloud data; determining a scaling factor between the 3D representationof the scene and the 3D point cloud representation of the scene, whereinthe determining of the scaling factor is based on a linear regressionmodel, a Gaussian distribution, or a Monte Carlo simulation; andperforming the alignment between the first vehicle sensor and the secondvehicle sensor based at least in part on the determined scaling factor.16. The computer program product of claim 15, wherein the determining ofthe scaling factor comprises filtering out one or more outliers from aset of scaling factors based on the linear regression model, theGaussian distribution, or the Monte Carlo simulation.
 17. The computerprogram product of claim 15, wherein the method comprises, in responseto filtering out the one or more outliers, determining a statisticalquantity; and performing the alignment based on the determinedstatistical quantity.
 18. The computer program product of claim 17,wherein the statistical quantity comprises a mean, median, or a mode.19. The computer program product of claim 15, wherein the performing ofthe alignment is further based on a determination of a rigidtransformation selected from candidate rigid transformations.
 20. Thecomputer program product of claim 19, wherein the method comprises,filtering outliers from the candidate rigid transformations.